Contrast maps for stimulus experience

Model (CESO)

parameter:
Cue onset,
ExpectationRating onset
Stimulus onset
OutcomeRating onset
Motion covariate: 24DOF + CSF
Spike combined into one regressor (Incorrect)

Table of Contents

Pain > (Vicarious & Cognitive) Pain > VC :: load dataset Pain > VC :: check data coverage Pain > VC :: Plot diagnostics, before l2norm Pain > VC :: run robfit Pain > VC :: remove outliers based on plot Pain > VC :: plot diagnostics, after l2norm Pain > VC :: ttest Pain > VC :: Neurosynth similarity Vicarious > (Pain & Cognitive) Vicarious > PC :: load dataset Vicarious > PC :: check data coverage Vicarious > PC :: Plot diagnostics, before l2norm Vicarious > PC :: run robfit Vicarious > PC :: remove outliers based on plot Vicarious > PC :: plot diagnostics, after l2norm Vicarious > PC :: ttest Vicarious > PC :: Neurosynth similarity Cognitive > (Pain & Vicarious) Cognitive > PV :: load dataset Cognitive > PV :: check data coverage Cognitive > PV :: Plot diagnostics, before l2norm Cognitive > PV :: run robfit Cognitive > PV :: remove outliers based on plot Cognitive > PV:: plot diagnostics, after l2norm Cognitive > PV :: ttest Cognitive > PV :: Neurosynth similarity

Pain > (Vicarious & Cognitive)

Pain > VC :: load dataset

mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_CESO/1stLevel';
con_list = dir(fullfile(mount_dir, '*/con_0005.nii'));
spm('Defaults','fMRI') 
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii
sampleto = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_CESO/1stLevel/sub-0013/con_0005.nii'
loading mask. mapping volumes. 
checking that dimensions and voxel sizes of volumes are the same. 
Pre-allocating data array. Needed: 39535452 bytes
Loading image number:    99
Elapsed time is 18.734762 seconds.
Image names entered, but fullpath attribute is empty. Getting path info.
Number of unique values in dataset: 9416678  Bit rate: 23.17 bits
contrast_name = {'cue_P', 'cue_V', 'cue_C', 'cue_G',...
    'stim_P', 'stim_V', 'stim_C', 'stim_G',...
    'motor', ...
    'simple_cue_P', 'simple_cue_V', 'simple_cue_C', ...
    'simple_stim_P', 'simple_stim_V', 'simple_stim_C'};

Pain > VC :: check data coverage

m = mean(con_data_obj);
m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);
orthviews(m, 'trans'); % display
SPM12: spm_check_registration (v7759)              16:44:46 - 12/01/2023
========================================================================
Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1
Grouping contiguous voxels:   1 regions

Pain > VC :: Plot diagnostics, before l2norm

drawnow; snapnow

[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj);

______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 3 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 20.20% Expected 4.95 outside 95% ellipsoid, found 6 Potential outliers based on mahalanobis distance: Bonferroni corrected: 3 images Cases 26 27 75 Uncorrected: 6 images Cases 6 26 27 35 75 99 Retained 6 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 41.41% Expected 4.95 outside 95% ellipsoid, found 7 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 7 images Cases 6 13 26 35 65 70 75 Mahalanobis (cov and corr, q<0.05 corrected): 3 images Outlier_count Percentage _____________ __________ global_mean 5 5.0505 global_mean_to_variance 0 0 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 4 4.0404 mahal_cov_uncor 6 6.0606 mahal_cov_corrected 3 3.0303 mahal_corr_uncor 7 7.0707 mahal_corr_corrected 0 0 Overall_uncorrected 9 9.0909 Overall_corrected 4 4.0404

SPM12: spm_check_registration (v7759) 16:45:23 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

Pain > VC :: run robfit

set(gcf,'Visible','on');

figure ('Visible', 'on');

drawnow, snapnow;

Pain > VC :: remove outliers based on plot

con = con_data_obj;
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
current length is 99
%for s = 1:length(wh_outlier_corr)
    %disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
%end
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 4 participants, size is now 95
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
    "participants that are outliers:... sub-0018"    "participants that are outliers:... sub-0043"    "participants that are outliers:... sub-0044"    "participants that are outliers:... sub-0102"
disp(n);
    {'sub-0018'}    {'sub-0043'}    {'sub-0044'}    {'sub-0102'}

Pain > VC :: plot diagnostics, after l2norm

imgs2 = con.rescale('l2norm_images');

Pain > VC :: ttest

t = ttest(imgs2);

One-sample t-test Calculating t-statistics and p-values

orthviews(t);

SPM12: spm_check_registration (v7759) 16:45:29 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

fdr_t = threshold(t, .05, 'fdr');

Image 1 FDR q < 0.050 threshold is 0.034056 Image 1 20 contig. clusters, sizes 1 to 67918 Positive effect: 39459 voxels, min p-value: 0.00000000 Negative effect: 28541 voxels, min p-value: 0.00000000

orthviews(fdr_t);

SPM12: spm_check_registration (v7759) 16:45:30 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

fdr_t = threshold(t, .001, 'fdr');

Image 1 FDR q < 0.001 threshold is 0.000508 Image 1 25 contig. clusters, sizes 1 to 50348 Positive effect: 28813 voxels, min p-value: 0.00000000 Negative effect: 21890 voxels, min p-value: 0.00000000

orthviews(fdr_t);

SPM12: spm_check_registration (v7759) 16:45:31 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

create_figure('montage'); axis off;

montage(fdr_t);

Setting up fmridisplay objects

sagittal montage: 1068 voxels displayed, 49635 not displayed on these slices

sagittal montage: 1094 voxels displayed, 49609 not displayed on these slices

sagittal montage: 1047 voxels displayed, 49656 not displayed on these slices

axial montage: 9511 voxels displayed, 41192 not displayed on these slices

axial montage: 10394 voxels displayed, 40309 not displayed on these slices

drawnow, snapnow;

Pain > VC :: Neurosynth similarity

[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( mean(imgs2), 'images_are_replicates', false, 'noverbose');
Input image 1
fullpath_was_empty
_____________________________________________________________________
    testr_low       words_low        testr_high     words_high  
    _________    ________________    __________    _____________

    -0.52465     {'visual'      }     0.36247      {'rating'   }
    -0.46463     {'object'      }     0.34118      {'sensation'}
    -0.43149     {'objects'     }     0.34025      {'pain'     }
    -0.34862     {'shape'       }     0.31548      {'ratings'  }
    -0.34789     {'attention'   }     0.31184      {'positive' }
    -0.33073     {'letter'      }     0.30252      {'negative' }
    -0.30522     {'perceptual'  }     0.29098      {'anxiety'  }
    -0.29873     {'color'       }     0.28151      {'trait'    }
    -0.29081     {'rotation'    }     0.27237      {'painful'  }
    -0.28689     {'orthographic'}     0.26573      {'age'      }

Pain > VC :: Pattern Phil

[obj, names] = load_image_set('pain_cog_emo');

Loaded images: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii

bpls_wholebrain = get_wh_image(obj, [8 16 24]);

names_wholebrain = names([8 16 24]);

create_figure('Kragel Pain-Cog-Emo maps', 1, 3);

stats = image_similarity_plot(imgs2, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');

Table of correlations Group:1 -------------------------------------- T-test on Fisher's r to Z transformed point-biserial correlations R_avg T P sig Pain Wholebrain 0.1610 28.8980 0.0000 1.0000 Cog Wholebrain -0.0118 -3.8539 0.0002 1.0000 Emo Wholebrain -0.1418 -26.6417 0.0000 1.0000

axis image

subplot(1, 3, 2)

barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)

Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} 0.16101 0.0054841 29.36 2.2204e-15 3.0123 {'Cog Wholebrain' } -0.011786 0.0030572 -3.8551 0.00021159 -0.39552 {'Emo Wholebrain' } -0.14183 0.0052638 -26.945 2.2204e-15 -2.7645

ans = struct with fields:

fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {95×3 cell} text_han: {95×3 cell} point_han: {95×3 cell} star_handles: [9.0001 10.0001 11.0001]

set(gca, 'FontSize', 14)

ylabel('Pattern similarity (r)');

title('Similarity (r) with patterns')

test_data_obj = resample_space(imgs2, bpls_wholebrain);

clear csim

for i = 1:3

csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');

end

Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297

subplot(1, 3, 3)

barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)

Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} 0.15579 0.005631 27.666 2.2204e-15 2.8385 {'Cog Wholebrain' } -0.012373 0.0029583 -4.1825 6.4718e-05 -0.42912 {'Emo Wholebrain' } -0.13788 0.0053476 -25.784 2.2204e-15 -2.6454

ans = struct with fields:

set(gca, 'FontSize', 14)

ylabel('Pattern similarity (cosine sim)');

title('Pattern response (cosine similarity)')

Vicarious > (Pain & Cognitive)

Vicarious > PC :: load dataset

mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_CESO/1stLevel';
con_list = dir(fullfile(mount_dir, '*/con_0006.nii'));
spm('Defaults','fMRI'); 
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii
sampleto = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_CESO/1stLevel/sub-0013/con_0006.nii'
loading mask. mapping volumes. 
checking that dimensions and voxel sizes of volumes are the same. 
Pre-allocating data array. Needed: 39535452 bytes
Loading image number:    99
Elapsed time is 17.147300 seconds.
Image names entered, but fullpath attribute is empty. Getting path info.
Number of unique values in dataset: 9377161  Bit rate: 23.16 bits
contrast_name = {'cue_P', 'cue_V', 'cue_C', 'cue_G',...
    'stim_P', 'stim_V', 'stim_C', 'stim_G',...
    'motor', ...
    'simple_cue_P', 'simple_cue_V', 'simple_cue_C', ...
    'simple_stim_P', 'simple_stim_V', 'simple_stim_C'};

Vicarious > PC :: check data coverage

m = mean(con_data_obj);
m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);
orthviews(m, 'trans') % display
SPM12: spm_check_registration (v7759)              17:41:14 - 12/01/2023
========================================================================
Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1
Grouping contiguous voxels:   1 regions
ans = 1×1 cell array
    {1×1 region}

Vicarious > PC :: Plot diagnostics, before l2norm

drawnow; snapnow;

[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj);

______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 3 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 37.37% Expected 4.95 outside 95% ellipsoid, found 3 Potential outliers based on mahalanobis distance: Bonferroni corrected: 1 images Cases 26 Uncorrected: 3 images Cases 26 27 79 Retained 6 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 44.44% Expected 4.95 outside 95% ellipsoid, found 7 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 7 images Cases 6 13 26 35 56 62 70 Mahalanobis (cov and corr, q<0.05 corrected): 1 images Outlier_count Percentage _____________ __________ global_mean 2 2.0202 global_mean_to_variance 0 0 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 1 1.0101 mahal_cov_uncor 3 3.0303 mahal_cov_corrected 1 1.0101 mahal_corr_uncor 7 7.0707 mahal_corr_corrected 0 0 Overall_uncorrected 9 9.0909 Overall_corrected 1 1.0101

SPM12: spm_check_registration (v7759) 17:41:45 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

Vicarious > PC :: run robfit

set(gcf,'Visible','on');
figure ('Visible', 'on');
drawnow, snapnow;

Vicarious > PC :: remove outliers based on plot

con = con_data_obj;
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
current length is 99
%for s = 1:length(wh_outlier_corr)
  %  disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
%end
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 1 participants, size is now 98
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
participants that are outliers:... sub-0043
disp(n);
sub-0043

Vicarious > PC :: plot diagnostics, after l2norm

imgs2 = con.rescale('l2norm_images');

Vicarious > PC :: ttest

t = ttest(imgs2);

One-sample t-test Calculating t-statistics and p-values

orthviews(t);

SPM12: spm_check_registration (v7759) 17:41:51 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

fdr_t = threshold(t, .05, 'fdr');

Image 1 FDR q < 0.050 threshold is 0.039036 Image 1 9 contig. clusters, sizes 1 to 77951 Positive effect: 20678 voxels, min p-value: 0.00000000 Negative effect: 57300 voxels, min p-value: 0.00000000

orthviews(fdr_t);

SPM12: spm_check_registration (v7759) 17:41:53 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

create_figure('montage'); axis off;

montage(fdr_t);

Setting up fmridisplay objects

sagittal montage: 1971 voxels displayed, 76007 not displayed on these slices

sagittal montage: 2024 voxels displayed, 75954 not displayed on these slices

sagittal montage: 1933 voxels displayed, 76045 not displayed on these slices

axial montage: 14565 voxels displayed, 63413 not displayed on these slices

axial montage: 15668 voxels displayed, 62310 not displayed on these slices

drawnow, snapnow;

Vicarious > PC :: Neurosynth similarity

[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( mean(imgs2), 'images_are_replicates', false, 'noverbose');
Input image 1
fullpath_was_empty
_____________________________________________________________________
    testr_low        words_low        testr_high      words_high   
    _________    _________________    __________    _______________

    -0.24204     {'motor'        }     0.36409      {'social'     }
    -0.22962     {'spatial'      }     0.30776      {'face'       }
    -0.20779     {'imagery'      }     0.30155      {'emotion'    }
    -0.20604     {'finger'       }     0.28793      {'facial'     }
    -0.19751     {'tactile'      }     0.28714      {'faces'      }
    -0.19716     {'execution'    }     0.28322      {'expression' }
    -0.19147     {'somatosensory'}     0.26992      {'expressions'}
    -0.18804     {'sensorimotor' }     0.25906      {'person'     }
    -0.18534     {'eye'          }     0.25482      {'story'      }
    -0.18293     {'hand'         }     0.25321      {'neutral'    }

Vicarious > PC :: Pattern Phil

[obj, names] = load_image_set('pain_cog_emo');
Loaded images:
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
stats = image_similarity_plot(imgs2, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
Table of correlations Group:1
--------------------------------------
T-test on Fisher's r to Z transformed point-biserial correlations
 	R_avg	T	P	sig	
Pain Wholebrain	-0.1401	-26.1970	0.0000	1.0000	
Cog Wholebrain	0.0173	5.9228	0.0000	1.0000	
Emo Wholebrain	0.1171	22.3240	0.0000	1.0000	
 
axis image
subplot(1, 3, 2)
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col   1: Pain Wholebrain	Col   2: Cog Wholebrain	Col   3: Emo Wholebrain	
---------------------------------------------
Tests of column means against zero
---------------------------------------------
           Name            Mean_Value    Std_Error       T           P         Cohens_d
    ___________________    __________    _________    _______    __________    ________

    {'Pain Wholebrain'}     -0.14014     0.0052757    -26.563    2.2204e-15    -2.6833 
    {'Cog Wholebrain' }     0.017331     0.0029259     5.9233    4.7949e-08    0.59835 
    {'Emo Wholebrain' }      0.11712      0.005208     22.489    2.2204e-15     2.2718 
ans = struct with fields:
         fig_han: [1×1 struct]
        axis_han: [1×1 Axes]
        bar_han1: [1×1 Bar]
         bar_han: {[1×1 Bar]  [1×1 Bar]  [1×1 Bar]}
    errorbar_han: {[1×1 ErrorBar]  [1×1 ErrorBar]  [1×1 ErrorBar]}
      point_han1: {98×3 cell}
        text_han: {98×3 cell}
       point_han: {98×3 cell}
    star_handles: [9.0004 10.0004 11.0004]
set(gca, 'FontSize', 14)
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
test_data_obj = resample_space(imgs2, bpls_wholebrain);
clear csim
for i = 1:3
    csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
end
Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 
Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 
Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 
subplot(1, 3, 3)
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col   1: Pain Wholebrain	Col   2: Cog Wholebrain	Col   3: Emo Wholebrain	
---------------------------------------------
Tests of column means against zero
---------------------------------------------
           Name            Mean_Value    Std_Error      T           P         Cohens_d
    ___________________    __________    _________    ______    __________    ________

    {'Pain Wholebrain'}     -0.13611       0.00538     -25.3    2.2204e-15    -2.5557 
    {'Cog Wholebrain' }     0.015667     0.0027803    5.6353    1.7122e-07    0.56925 
    {'Emo Wholebrain' }        0.116     0.0052411    22.133    2.2204e-15     2.2358 
ans = struct with fields:
         fig_han: [1×1 struct]
        axis_han: [1×1 Axes]
        bar_han1: [1×1 Bar]
         bar_han: {[1×1 Bar]  [1×1 Bar]  [1×1 Bar]}
    errorbar_han: {[1×1 ErrorBar]  [1×1 ErrorBar]  [1×1 ErrorBar]}
      point_han1: {98×3 cell}
        text_han: {98×3 cell}
       point_han: {98×3 cell}
    star_handles: [12.0004 13.0004 14.0004]
set(gca, 'FontSize', 14)
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')

Cognitive > (Pain & Vicarious)

Cognitive > PV :: load dataset

mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_CESO/1stLevel';
con_list = dir(fullfile(mount_dir, '*/con_0007.nii'));
spm('Defaults','fMRI');
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii
sampleto = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_CESO/1stLevel/sub-0013/con_0007.nii'
loading mask. mapping volumes. 
checking that dimensions and voxel sizes of volumes are the same. 
Pre-allocating data array. Needed: 39535452 bytes
Loading image number:    99
Elapsed time is 17.159734 seconds.
Image names entered, but fullpath attribute is empty. Getting path info.
Number of unique values in dataset: 9413473  Bit rate: 23.17 bits
contrast_name = {'cue_P', 'cue_V', 'cue_C', 'cue_G',...
    'stim_P', 'stim_V', 'stim_C', 'stim_G',...
    'motor', ...
    'simple_cue_P', 'simple_cue_V', 'simple_cue_C', ...
    'simple_stim_P', 'simple_stim_V', 'simple_stim_C'};

Cognitive > PV :: check data coverage

m = mean(con_data_obj);

m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);

orthviews(m, 'trans'); % display

SPM12: spm_check_registration (v7759) 17:42:53 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

Cognitive > PV :: Plot diagnostics, before l2norm

drawnow; snapnow;

[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj);

______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 4 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 34.34% Expected 4.95 outside 95% ellipsoid, found 7 Potential outliers based on mahalanobis distance: Bonferroni corrected: 1 images Cases 26 Uncorrected: 7 images Cases 6 26 27 37 61 63 99 Retained 6 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 45.45% Expected 4.95 outside 95% ellipsoid, found 7 Potential outliers based on mahalanobis distance: Bonferroni corrected: 1 images Cases 26 Uncorrected: 7 images Cases 13 26 27 41 68 75 83 Mahalanobis (cov and corr, q<0.05 corrected): 1 images Outlier_count Percentage _____________ __________ global_mean 5 5.0505 global_mean_to_variance 5 5.0505 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 0 0 mahal_cov_uncor 7 7.0707 mahal_cov_corrected 1 1.0101 mahal_corr_uncor 7 7.0707 mahal_corr_corrected 1 1.0101 Overall_uncorrected 12 12.121 Overall_corrected 1 1.0101

SPM12: spm_check_registration (v7759) 17:43:29 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

Cognitive > PV :: run robfit

set(gcf,'Visible','on');
figure ('Visible', 'on');
drawnow, snapnow;

Cognitive > PV :: remove outliers based on plot

con = con_data_obj;
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
current length is 99
%for s = 1:length(wh_outlier_corr)
   % disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
%end
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 1 participants, size is now 98
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
participants that are outliers:... sub-0043
disp(n);
sub-0043

Cognitive > PV:: plot diagnostics, after l2norm

imgs2 = con.rescale('l2norm_images');

Cognitive > PV :: ttest

t = ttest(imgs2);

One-sample t-test Calculating t-statistics and p-values

orthviews(t);

SPM12: spm_check_registration (v7759) 17:43:34 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

fdr_t = threshold(t, .05, 'fdr');

Image 1 FDR q < 0.050 threshold is 0.040001 Image 1 10 contig. clusters, sizes 1 to 79902 Positive effect: 43968 voxels, min p-value: 0.00000000 Negative effect: 35947 voxels, min p-value: 0.00000000

orthviews(fdr_t);

SPM12: spm_check_registration (v7759) 17:43:35 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

create_figure('montage'); axis off;

montage(fdr_t);

Setting up fmridisplay objects

sagittal montage: 2039 voxels displayed, 77876 not displayed on these slices

sagittal montage: 1978 voxels displayed, 77937 not displayed on these slices

sagittal montage: 1983 voxels displayed, 77932 not displayed on these slices

axial montage: 14978 voxels displayed, 64937 not displayed on these slices

axial montage: 16350 voxels displayed, 63565 not displayed on these slices

drawnow, snapnow;

Cognitive > PV :: Neurosynth similarity

[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( mean(imgs2), 'images_are_replicates', false, 'noverbose');
Input image 1
fullpath_was_empty
_____________________________________________________________________
    testr_low      words_low      testr_high       words_high   
    _________    _____________    __________    ________________

    -0.34988     {'rating'   }     0.47063      {'visual'      }
    -0.32866     {'trait'    }     0.34914      {'shape'       }
    -0.32835     {'social'   }     0.33765      {'object'      }
     -0.3271     {'emotion'  }     0.33052      {'attention'   }
    -0.32161     {'affect'   }     0.32404      {'eye'         }
    -0.31639     {'person'   }     0.31858      {'letter'      }
    -0.30788     {'negative' }     0.31375      {'visuospatial'}
    -0.30525     {'positive' }     0.30483      {'spatial'     }
     -0.3037     {'affective'}      0.3042      {'color'       }
    -0.30052     {'ratings'  }     0.30405      {'objects'     }

Cognitive > PV :: Pattern Phil

[obj, names] = load_image_set('pain_cog_emo');

bpls_wholebrain = get_wh_image(obj, [8 16 24]);

names_wholebrain = names([8 16 24]);

create_figure('Kragel Pain-Cog-Emo maps', 1, 3);

stats = image_similarity_plot(imgs2, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');

Table of correlations Group:1 -------------------------------------- T-test on Fisher's r to Z transformed point-biserial correlations R_avg T P sig Pain Wholebrain -0.0445 -12.9986 0.0000 1.0000 Cog Wholebrain 0.0006 0.2027 0.8398 0.0000 Emo Wholebrain 0.0416 11.9365 0.0000 1.0000

axis image

subplot(1, 3, 2)

barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)

Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} -0.044452 0.0034145 -13.019 2.2204e-15 -1.3151 {'Cog Wholebrain' } 0.00055217 0.0027213 0.20291 0.83963 0.020497 {'Emo Wholebrain' } 0.041572 0.0034793 11.948 2.2204e-15 1.207

ans = struct with fields:

fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {98×3 cell} text_han: {98×3 cell} point_han: {98×3 cell} star_handles: [9.0005 10.0005 11.0005]

set(gca, 'FontSize', 14)

ylabel('Pattern similarity (r)');

title('Similarity (r) with patterns')

test_data_obj = resample_space(imgs2, bpls_wholebrain);

clear csim

for i = 1:3

csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');

end

Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297

subplot(1, 3, 3)

barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)

Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} -0.043637 0.003392 -12.865 2.2204e-15 -1.2995 {'Cog Wholebrain' } 0.0025263 0.0026243 0.96267 0.33811 0.097244 {'Emo Wholebrain' } 0.039486 0.0034385 11.484 2.2204e-15 1.16

ans = struct with fields:

fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {98×3 cell} text_han: {98×3 cell} point_han: {98×3 cell} star_handles: [12.0005 13.0005 14.0005]

set(gca, 'FontSize', 14)

ylabel('Pattern similarity (cosine sim)');

title('Pattern response (cosine similarity)')

drawnow, snapnow;

% save html
pubdir = pwd;
pubfilename = 's03_PVC_stim_contrasts_ROI.mlx';
p = struct('useNewFigure', false, 'maxHeight', 800, 'maxWidth', 800, ...
  'format', 'html', 'outputDir', pubdir, ...
  'showCode', true, 'stylesheet', which('mxdom2simplehtml_CANlab.xsl'));
htmlfile = publish(pubfilename, p);